Skip to content
No description, website, or topics provided.
Branch: master
Clone or download
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
datasets add reduced-proportion random-attack training set May 5, 2018
experiments add reduced-proportion random-attack training set May 5, 2018
models increase training iterations Apr 30, 2018
nets/usts/ZF
py-faster-rcnn add reduced-proportion random-attack training set May 5, 2018
.gitattributes
.gitignore add essential files back for running caffe test May 2, 2018
.gitmodules original faster-rcnn Apr 28, 2018
README.md add random attack experiments May 3, 2018

README.md

BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain

By Tianyu Gu, Brendan Dolan-Gavitt, Siddharth Garg

paper here code WIP

Installation

  1. Clone the BadNets repository.

    git clone https://github.com/Kooscii/BadNets.git
  2. Complete the installation under py-faster-rcnn first.

  3. Download US Traffic Signs (usts) dataset by running fetch_usts.py.

    cd $BadNets/datasets
    python fetch_usts.py

    Go here for more information about the usts dataset.

  4. Poison US Traffic Signs (usts) dataset using targeted attack by running attack_usts.py with 'targeted' argument.

    cd $BadNets/datasets
    python attack_usts.py targeted
  5. Poison US Traffic Signs (usts) dataset using random attack by running attack_usts.py with 'random' argument.

    cd $BadNets/datasets
    python attack_usts.py random

Testing

  1. Download our trained clean and backdoored models. Extract and put it under $BadNets folder.

    $BadNets
    ├── datasets
    ├── experiments
    ├── models
    │   ├── *.caffemodel    # put caffemodels here
    │   └── ...
    ├── nets
    ├── py-faster-rcnn
    └── README.md
  2. To test a model, use the following command. Please refer to experiments/test.sh for more detail.

    cd $BadNets
    ./experiments/test.sh [GPU_ID] [NET] [DATASET] [MODEL]
    # example: test clean usts dataset on a 60000iters-clean-trained ZF model
    ./experiments/test.sh 0 ZF usts_clean usts_clean_60000

Training

  1. Download pre-trained ImageNet models

    cd $BadNets/py-faster-rcnn
    ./data/scripts/fetch_imagenet_models.sh
  2. To train a model, use the following command. Please refer to experiments/train.sh for more detail.

    cd $BadNets
    ./experiments/train.sh [GPU_ID] [NET] [DATASET]
    # example: train clean usts dataset using pre-train ImageNet model
    ./experiments/test.sh 0 ZF usts_clean

    Model snapshots will be saved under ./py-faster-rcnn/output/$DATASET. The final model will be copy to ./models and rename to $DATASET.caffemodel

Notes

  1. Faster-RCNN uses caches for annotations. Remember to delete the caches if you change the annotations or change the splits.
    rm -rf ./py-faster-rcnn/data/cache          # training cache
    rm -rf ./datasets/usts/annotations_cache    # testing cache

Results

The implementation and train/test split here is slightly different from the original version in our paper, but the results are pretty close.

  1. Targeted Attack

    class\model clean baseline yellow square bomb flower
    stop 89.1 86.8 88.6 89.0 test on purely clean set
    speedlimit 83.3 82.1 84.1 84.1 test on purely clean set
    warning 91.8 90.5 91.3 91.4 test on purely clean set
    stop -> speedlimit <1.5 90.9 91.9 92.1 test on purely poisoned set
You can’t perform that action at this time.